Google Data Engineer - GCP-DE certification

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

2. Which is not a valid reason for poor Cloud Bigtable performance?

A) The table's schema is not designed correctly.
B) There are issues with the network connection.
C) The workload isn't appropriate for Cloud Bigtable.
D) The Cloud Bigtable cluster has too many nodes.

3. You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

A) Cloud SQL
B) Cloud Spanner
C) Cloud Bigtable
D) Cloud Datastore

4. You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A) Create an application that writes to a Cloud SQL database to store the dat
B) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C) Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D) Create an application that provides an AP
E) Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
F) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

5. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Partition the tables by a column containing a TIMESTAMP or DATE Type.
B) Re-create the tables using DD
C) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
D) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
E) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
F) Write an Apache Beam pipeline that creates a BigQuery table per data

Solutions: